Invariance in radial basis function neural networks in human face classification
Neural Processing LettersPublished 1 June 1995
A. Jonathan Howell, Hilary Buxton
Citations33
SJR quartileQ2
SJR score0.67
SNIP0.92
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TL;DR
Experiments using face images in ranges from face-on to profile show the RBF network's invariance to 2-D shift, scale and y-axis rotation.
Abstract
This paper is concerned with the types of invariance exhibited by Radial Basis Function (RBF) neural networks when used for human face classification, and the generalisation abilities arising from this behaviour. Experiments using face images in ranges from face-on to profile show the RBF network's invariance to 2-D shift, scale and y-axis rotation. Finally, the suitability of RBF techniques for future, more automated face classification purposes is discussed.
Keywords
Computer Science
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Neural Processing LettersInvariance in radial basis function neural networks in human face classification
33 Citations1995A. Jonathan Howell, Hilary Buxton
Experiments using face images in ranges from face-on to profile show the RBF network's invariance to 2-D shift, scale and y-axis rotation.